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Recent Technological Advances in the Control and Guidance of Ships

Published online by Cambridge University Press:  21 October 2009

N. A. J. Witt ,
R. Sutton  and
K. M. Miller
N. A. J. Witt
Affiliation:
(Marine Dynamics Research Group, Institute of Marine Studies, University of Plymouth)
R. Sutton
Affiliation:
(Marine Dynamics Research Group, Institute of Marine Studies, University of Plymouth)
K. M. Miller
Affiliation:
(Marine Dynamics Research Group, Institute of Marine Studies, University of Plymouth)
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Abstract

Over the past seventy years many advances have been made in the field of ship control. Early developments by Sperry and Minorsky on proportional controllers have led to today's modern control systems which have interfacing capabilities with position fixing equipment.

This paper presents a brief historical summary of the methods employed in ship control from early proportional devices through the range of adaptive systems and concludes with details of a possible future control method known as intelligent control.

Intelligent control consists of three methodologies: expert, fuzzy and neural. An investigation and comparison of the methodologies will present possible future control strategies.

Keywords

1. Autopilots2. Control3. Expert systems4. Fuzzy logic5. Neural networks
Type
Research Article
Information
The Journal of Navigation , Volume 47 , Issue 2 , May 1994 , pp. 236 - 258
Copyright
Copyright © The Royal Institute of Navigation 1994

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References

REFERENCES

1Sperry, E. (1922). Directional stability of automatically steered bodies. Journal of the American Society of Naval Engineers, 42, 2.Google Scholar
2Minorsky, N. (1922). Automatic Steering. Proc. the thirtieth general meeting of the Society of Naval Architects and Marine Engineers. November 8–9, New York.Google Scholar
3Mort, N. (1983). Autopilot design for surface ship steering using self?tuning controller algorithms. PhD Thesis, University of Sheffield.Google Scholar
4Sugimoto, A. and Kojima, T. (1978). A new autopilot system with condition adaptivity. Proceedings 5th Ship Control Systems Symposium. Annapolis, Maryland, USA.Google Scholar
5Merlo, P. and Tiano, A. (1975). Experiments about computer controller ship steering. Semana International Sobre la Automatica en la Marina. Barcelona, Spain.Google Scholar
6Ohtsu, K., Horigome, M. and Kitagawa, G. (1979). A new ship's autopilot design through a stochastic model. Automatica, 15, 255–268.CrossRefGoogle Scholar
7Van Amerongen, J. (1982). Adaptive steering of ships: a model reference approach to improved manoeuvring and economical course?keeping. PhD Thesis, Delft University of Technology.Google Scholar
8Clarke, D. W. and Gawthrop, P. J. (1975). Self?tuning controller. Proceedings IEE, 122, Part?D.Google Scholar
9Lim, C. C. and Forsythe, W. (1983). Autopilot for ship control. Proceedings IEE, 130, Part?D.Google Scholar
10Astrom, K. J. and Wittenmark, B. (1973). On self?tuning regulators. Automatica 9, 185?199.CrossRefGoogle Scholar
11Kallstrom, C. G. (1979). Identification and adaptive control applied to ship steering. PhD Thesis, Lund Institute of Technology, Sweden.Google Scholar
12Katebi, M. R. and Byrne, J. C. (1988). LQG adaptive ship autopilot. Trans. Inst. MC, 10 (4), 187–197.CrossRefGoogle Scholar
13Motora, S. (1953). On the automatic steering and yawing of ships in rough seas. Journal Society of Naval Architects. Japan, 94.Google Scholar
14Norrbin, N. H. (1972). On the added resistance due to steering on a straight course. Proc. 13th ITTC, Berlin/Hamburg.Google Scholar
15Akaike, H. (1969). Fitting autoregressive models for prediction. Ann. Inst. Statist. Math. Tokyo, 21, 243.CrossRefGoogle Scholar
16Akaike, H. (1971). Autoregressive model fitting for control. Ann. Inst. Statist. Math. Tokyo, 23, 163.CrossRefGoogle Scholar
17St. Denis, M. and Pierson, W. J. (1953). The motion of ships in confused seas. Trans. SNAME.Google Scholar
18Harris, C. J. and Brown, M. (1991). Intelligent control for autonomous guided vehicles. IEE Colloquium on Intelligent Control.Google Scholar
19Barr, A. and Feigenbaum, E. A. (1982). The Handbook of Artificial Intelligence, Vol. 2. William Kaufmann. Los Altos, California.Google Scholar
20Davis, R. (1982). Expert systems. AI Magazine. Spring, 3–22.Google Scholar
21Sutton, R. and Jess, I. M. (1991). A design study of a self?organizing fuzzy autopilot for ship control. Proc Instn Mech Engrs. 205, pt 1, 35–47.Google Scholar
22Efstathiou, J. (1988). Expert systems, fuzzy logic and rule?based control explained at last. Trans Inst MC. 100, 198–206.CrossRefGoogle Scholar
23Procyk, T. J. and Mamdami, E. H. (1979). A linguistic self?organizing process controller. Automatica, 15, 15–30.CrossRefGoogle Scholar
24Shoa, S. (1988). Fuzzy self?organizing controller and its application for dynamic processes. Fuzzy Sets and Systems, 26, 151–164.CrossRefGoogle Scholar
25Mandic, N. J., Scharf, E. M. and Mamdani, E. H. (1985). Practical application of a heuristic fuzzy rule?based controller to the dynamic control of a robot area. Proc IEE, 132, part D, no. 4, 190–203.CrossRefGoogle Scholar
26Tanscheit, R. and Scharf, E. M. (1988). Experiments with the use of a rule?based self?organizing controller for robotic applications. Fuzzy Sets and Systems, 26, 191–214.CrossRefGoogle Scholar
27Daley, S. and Gill, K. F. (1989). Comparison of a fuzzy logic controller with a P+D control law. ASME Trans: Journal of Dynamic Systems, Measurement and Control, vol. III, no. 2, 128?137.Google Scholar
28Sugiyama, K. (1988). Rule?based self?organizing controller, in fuzzy computing. (Gapta, M. M. and Yamakawa, T. (eds), Elsevier Science Publishers BV. (North Holland).Google Scholar
29van Amerongen, J., van Nauta Lemke, H. R. and van der Veen, J. C. T. (1977). An autopilot for ships designed with fuzzy sets. Proc. IFAC Conference on Digital Computer Applications to Process Control. The Hague.CrossRefGoogle Scholar
30James, M. K. (1986). Modelling the decision process in computer simulation of ship navigation. This Journal, 39, 32.Google Scholar
31Hasegawa, K. (1987). Automatic collision avoidance systems for ships using fuzzy control. Proceedings 8th Ship Control Symposium, vol. 2, The Hague.Google Scholar
32Pedrycz, W. (1993). Fuzzy control and fuzzy systems. Research Studies Press, Taunton.Google Scholar
33Lee, C. C. (1990). Fuzzy logic in control systems: fuzzy logic controllers – Pts I and II. IEEE Trans Syst Man Cyb, 20 (2), 404–435.CrossRefGoogle Scholar
34Pierson, W. J. and Moscowitz, L. (1964). A proposed spectral form for fully developed seas based on a similarity theory of Kitaigorodskii, S. A.. Journal of Geophysical Research, 69.CrossRefGoogle Scholar
35Harris, C. J. and Read, A. B. (1988). Knowledge?based fuzzy motion control of autonomous vehicles. Proc IFAC Conference on Artificial Intelligence. Swansea.Google Scholar
36Daley, S and Gill, K. F. (1986). A design study of a self?organizing fuzzy logic controller. Procs Instn Mech Eng, 200 (Ci), 59–69.Google Scholar
37Gawthrop, P. J., Sharbaro, D. and Hunt, K. J. (1992). Connectionist architectures for control systems. Automatica 28 (6), 1083–1112.Google Scholar
38Witt, N. (1993). A neural network autopilot for ship guidance. Marine Dynamics Research Group Technical Report No. 93004. Institute of Marine Studies, University of Plymouth.Google Scholar
39Witt, N. and Miller, K. M. (1993). A neural network autopilot for ship control. Proceedings Maritime Communications and Control. London.Google Scholar
40Cybenko, G. (1989). Approximations by superpositions of a Sigmoidal Function. Math. Control Signal Systems, 2, 303–314.CrossRefGoogle Scholar
41Chester, D. (1990). Why two hidden layers are better than one. IEEE Int. Joint Conf. on Neural Networks, 265–268.Google Scholar
42Sharbaro, D. and Gawthrop, P. J. (1990). Learning complex mappings by stochastic approximation. Int. Joint Conference on Neural Networks, 569–572.Google Scholar
43Hecht?Nielson, R. (1987). Counterpropagation networks. Applied Optics, 26, 4979?4984.CrossRefGoogle Scholar
44Kohenon, T. (1987). Self?Organization and Associative Memory. Springer?Verlag, Berlin.Google Scholar
45Albus, J. S. (1975a). Data storage in the Cerebellar Model Articulation Controller (CMAC). J. Dynamic Systems, Measurement and Control, 97, 228–233.CrossRefGoogle Scholar
46Albus, J. S. (1975b). A new approach to manipulator: The Cerebellar Model Control. J. Dynamic Systems, Measurement and Control, 97, 220–227.CrossRefGoogle Scholar
47Ersu, E. and Tolle, H. (1984). A new concept for learning control inspired by brain theory. Proc. 9th World Congress of IFAC, 7, 245–250.Google Scholar
48Simpson, P. K. (1989). Artificial Neural Systems. Pergamon Press, New York.Google Scholar
49Tapp, N. (1989). A non?dimensional mathematical model for use in marine simulators. MPhil Thesis, Plymouth Polytechnic.Google Scholar
50Witt, N. and Miller, K. M. (1993b). An adaptive track keeping neural network controller for ship guidance. Proceedings of The Impact of New Technology on the Marine Industries. Warsash.Google Scholar
51Blanke, M. (1981). Ship propulsion losses related to automatic steering and prime mover control. PhD Thesis, Technical University of Denmark.Google Scholar
52Clarke, D. (1982). Do autopilots save fuel? Proceedings IMarE(C), Paper C97, 94.Google Scholar